Database Learning: Toward a Database that Becomes Smarter Every Time
Summary: Database Learning: AQP that improves with each query by learning from past answers and exploiting shared distributions. Verdict on Spark SQL uses maximum entropy to yield tighter estimates; real traces show 73.7% coverage and up to 23x speedups. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yongjoo Park (University of Michigan)
- 2. Ahmad Shahab Tajik (University of Michigan)
- 3. Michael Cafarella (University of Michigan)
- 4. Barzan Mozafari (University of Michigan)
BibTeX Citation
@inproceedings{park_sigmod17,
title = {{Database Learning: Toward a Database that Becomes Smarter Every Time}},
author = {Park, Yongjoo and Tajik, Ahmad Shahab and Cafarella, Michael and Mozafari, Barzan},
series = {{SIGMOD} '17},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3035918.3064013},
url = {https://dl.acm.org/doi/10.1145/3035918.3064013},
year = {2017}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 39 of 39 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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